# Evaluation Methods and Best Practices

Evaluation strategies, metrics, and best practices for fine-tuning LangGraph applications.

**💡 Tip**: For practical evaluation scripts and report templates, see [examples.md](examples.md#phase-2-baseline-evaluation-examples).

## 📚 Table of Contents

This guide is divided into the following sections:

### 1. [Evaluation Metrics Design](./evaluation_metrics.md)
Learn how to define and calculate metrics used for evaluation.

### 2. [Test Case Design](./evaluation_testcases.md)
Understand test case structure, coverage, and design principles.

### 3. [Statistical Significance Testing](./evaluation_statistics.md)
Master methods for multiple runs and statistical analysis.

### 4. [Evaluation Best Practices](./evaluation_practices.md)
Provides practical evaluation guidelines.

## 🎯 Quick Start

### For First-Time Evaluation

1. **[Understand Evaluation Metrics](./evaluation_metrics.md)** - Which metrics to measure
2. **[Design Test Cases](./evaluation_testcases.md)** - Create representative cases
3. **[Learn Statistical Methods](./evaluation_statistics.md)** - Importance of multiple runs
4. **[Follow Best Practices](./evaluation_practices.md)** - Effective evaluation implementation

### Improving Existing Evaluations

1. **[Add Metrics](./evaluation_metrics.md)** - More comprehensive evaluation
2. **[Improve Coverage](./evaluation_testcases.md)** - Enhance test cases
3. **[Strengthen Statistical Validation](./evaluation_statistics.md)** - Improve reliability
4. **[Introduce Automation](./evaluation_practices.md)** - Continuous evaluation pipeline

## 📖 Importance of Evaluation

In fine-tuning, evaluation provides:
- **Quantifying Improvements**: Objective progress measurement
- **Basis for Decision-Making**: Data-driven prioritization
- **Quality Assurance**: Prevention of regressions
- **ROI Demonstration**: Visualization of business value

## 💡 Basic Principles of Evaluation

For effective evaluation:

1. ✅ **Multiple Metrics**: Comprehensive assessment of quality, performance, cost, and reliability
2. ✅ **Statistical Validation**: Confirm significance through multiple runs
3. ✅ **Consistency**: Evaluate with the same test cases under the same conditions
4. ✅ **Visualization**: Track improvements with graphs and tables
5. ✅ **Documentation**: Record evaluation results and analysis

## 🔍 Troubleshooting

### Large Variance in Evaluation Results
→ Check [Statistical Significance Testing](./evaluation_statistics.md#outlier-detection-and-handling)

### Evaluation Takes Too Long
→ Implement staged evaluation in [Best Practices](./evaluation_practices.md#troubleshooting)

### Unclear Which Metrics to Measure
→ Check [Evaluation Metrics Design](./evaluation_metrics.md) for purpose and use cases of each metric

### Insufficient Test Cases
→ Refer to coverage analysis in [Test Case Design](./evaluation_testcases.md#test-case-design-principles)

## 📋 Related Documentation

- **[Prompt Optimization](./prompt_optimization.md)** - Techniques for prompt improvement
- **[Examples Collection](./examples.md)** - Samples of evaluation scripts and reports
- **[Workflow](./workflow.md)** - Overall fine-tuning flow including evaluation
- **[SKILL.md](./SKILL.md)** - Overview of the fine-tune skill

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**💡 Tip**: For practical evaluation scripts and templates, see [examples.md](examples.md#phase-2-baseline-evaluation-examples).